Dr David S. Holder

Clinical neurophysiology

MBBS

In practice since
1979

Male

Speaks:English

Consultation fee:

Professor Holder is a Professor of Biophysics and Clinical Neurophysiology and an Honorary Consultant in Clinical Neurophysiology at University College London & UCL Hospitals. He has had joint training in Clinical Medicine and Biophysics and as a younger doctor became interested in developing a new method which could image fast electrical activity in the brain. During a Masters degree at Berkeley, he came up with a practical possible way to realise this, the then new method of Electrical Impeda...

Professor Holder is a Professor of Biophysics and Clinical Neurophysiology and an Honorary Consultant in Clinical Neurophysiology at University College London & UCL Hospitals.

He has had joint training in Clinical Medicine and Biophysics and as a younger doctor became interested in developing a new method which could image fast electrical activity in the brain. During a Masters degree at Berkeley, he came up with a practical possible way to realise this, the then new method of Electrical Impedance Tomography (EIT). With a MRC training and then Royal Society University Research Fellowship held at University College London, Professor Holder built up an interdisciplinary research group which has pioneered the application of EIT for imaging brain function. This is currently based in the department of Medical Physics. Current main activities are in developing EIT for use in imaging fast neural activity in the brain and to provide early imaging at the point of collection in acute stroke and so permit rapid use of clot-dissolving agents.

He has been a Consultant in Clinical Neurophysiology at the Middlesex Hospital and UCH since 1997 where Ihe undertakes EEG reporting, nerve conduction studies and EMG (electromyography). He has an interest in the development of new methods for diagnosis, such as vibration studies for RSI (repetitive strain injury), ambulatory nerve conduction studies for functional nerve entrapments which cause pain on exercise, and using machine learning methods for automated analysis of the EEG.